from IPython import display
display.Image('team.png', width=700)
display.Image('./old_work_flow.jpg')
Simpler workflow?
Not a professional programmer but still want to write cool analysis in Python and make it as fast as c++? (ask Dan?)
Read new trajectory format that cpptraj does not support? (write XTC file parser with 3 lines of codes?)
Write parallel code for analysis? (Hint: how we can make parallel calculation with only one line of code?)
Interface with other programs? (pyrosetta, rdkit, cclib, …)
# data: http://www.amber.utah.edu/AMBER-workshop/London-2015/DNA-tutorial/
import pytraj as pt
traj0 = pt.load('md.nc', 'dna.prmtop')
traj0
traj = traj0.autoimage()['!:WAT']
traj
# compute rmsd and convert raw data to pandas' DataFrame
data = pt.rmsd(traj, ref=0, mask=':1-14&!@H=', dtype='dataframe')
data.head(5)
%matplotlib inline
data.plot()
data.hist()
traj = pt.iterload(['tz2.0.nc', 'tz2.1.nc', 'tz2.2.nc'], 'tz2.parm7')
data = pt.pmap(pt.rmsd, traj, ref=0, mask='@CA', n_cores=8)
# MPI
# data = pt.pmap_mpi(pt.rmsd, traj, ref=0, mask='@CA')
# serial: data = pt.rmsd(traj, ref=0, mask='@CA')
from IPython import display
display.Image('bench_pmap_casegroup.png', width=500)
traj2 = pt.iterload('tz2.nc', 'tz2.parm7')
energies = pt.energy_decomposition(traj2, igb=8, dtype='dataframe')
energies[['bond', 'angle', 'dihedral', 'gb']].head()
# XTC
# amber.conda install mdtraj # 10 second to install
import mdtraj as md
t0 = md.load('monolayer.xtc', top='monolayer.pdb')
coordinates = t0.xyz.astype('f8')
traj = pt.Trajectory(xyz=coordinates, top='monolayer.pdb')
pt.center_of_mass(traj)
help(pt.calc_center_of_mass)
display.Image('./3pqr_nglview.png')
display.Image('./trpzip2_nglview.png')
amber.pip install nglview